Different from the existing DRL algorithms which consider either purely discrete [1]}, [2]}, [3]} or purely continuous actions [4]}, [5]}, [6]}, we here study a more practical DRL setting with a hybrid discrete-continuous action for improving the training performance. Even though such a hybrid action setting has been previously mentioned in a few related works such as [7]}, a holistic investigation on the sampling of discrete and continuous actions has not been given. blackTherefore, we propose a new parameterized advantage actor critic (A2C) algorithm to optimize the system latency with actor and critic designs. The actor aims to build a near-optimal parameterized offloading strategy for MDs, while the critic estimates the value function to guide the policy update of the actor.
